A human information processing theory of the interpretation of visualizations: demonstrating its utility
Providing an approach to model the memory structures that humans build as they use visualizations could be useful for researchers, designers and educators in the field of information visualization. Cheng and colleagues formulated Representation Interpretive Structure Theory (RIST) for that purpose. RIST adopts a human information processing perspective in order to address the immediate, short timescale, cognitive load likely to be experienced by visualization users. RIST is operationalized in a graphical modeling notation and browser-based editor. This paper demonstrates the utility of RIST by showing that (a): RIST models are compatible with established empirical and computational cognitive findings about differences in human performance on alternative representations; (b) they can encompass existing explanations from the literature; and, (c) they provide new explanations about causes of those performance differences.
Funding
Automating Representation Choice for AI Tools : EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL
How to (re)represent it? : EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL | EP/R030642/1
History
Publication status
- Published
File Version
- Published version
Journal
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing SystemsPublisher
ACMPublisher URL
External DOI
Article number
194Pages
14Event name
CHI '24: CHI Conference on Human Factors in Computing SystemsEvent location
Honolulu, HawaiiEvent type
conferenceEvent start date
2024-05-11Event finish date
2024-05-16Book title
CHI '24: Proceedings of the CHI Conference on Human Factors in Computing SystemsISBN
9798400703300Department affiliated with
- Informatics Publications
Institution
University of SussexFull text available
- Yes
Peer reviewed?
- Yes